This is Python Tutorial

This is our first program in python: It is just started here

print("Hello World")

Hello World

$a=b+c$

Variables

x = 3

%whos # what variable is saved so far

Variable   Type    Data/Info
----------------------------
x          int     3

print(type(x))

<class 'int'>

x = 5.7

%whos

Variable   Type     Data/Info
-----------------------------
x          float    5.7

print(type(x))

<class 'float'>

abcd = 556.32

%whos

Variable   Type     Data/Info
-----------------------------
abcd       float    556.32
x          float    5.7

a,b,c,d,f = 3,5,6.0,7.2,-3

%whos

Variable   Type     Data/Info
-----------------------------
a          int      3
abcd       float    556.32
b          int      5
c          float    6.0
d          float    7.2
f          int      -3
x          float    5.7

del abcd

%whos

Variable   Type     Data/Info
-----------------------------
a          int      3
b          int      5
c          float    6.0
d          float    7.2
f          int      -3
x          float    5.7

print(abcd)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[13], line 1
----> 1 print(abcd)

NameError: name 'abcd' is not defined

c = 2+4j

print(type(c))

<class 'complex'>

s = "hellow how are you"

print(type(s))

<class 'str'>

Operators

%whos

Variable   Type       Data/Info
-------------------------------
a          int        3
b          int        5
c          complex    (2+4j)
d          float      7.2
f          int        -3
s          str        hellow how are you
x          float      5.7

sumOfaAndb = a+b #variables name should give you the look and feel what the data has

print(sumOfaAndb)

8

type(sumOfaAndb)

int

type(a+d) #float is super set than int and Python follows the super set

float

v = ((a+d)**3)/4

print(v)

265.30199999999996

s1 = "hellow"
s2 = "world"
s = s1+s2
print(s)

hellowworld

10//3 #quotient

3

10/3

3.3333333333333335

_ # stores the result of the above expression

3.3333333333333335

3x = 5 #can a variable name start with a digit i.e. 3x? NO

  Cell In[30], line 1
    3x = 5
    ^
SyntaxError: invalid decimal literal

@y = 4 #can't start a variable name with @

  Cell In[31], line 1
    @y = 4
     ^
SyntaxError: invalid syntax. Maybe you meant '==' or ':=' instead of '='?

*t=4 #can't start a variable name with * including special sign other then _

  Cell In[32], line 1
    *t=4
    ^
SyntaxError: starred assignment target must be in a list or tuple

_e = 6 # not recommended

startingTimeOfTheCourse = 2.0

%whos

Variable                  Type       Data/Info
----------------------------------------------
a                         int        3
b                         int        5
c                         complex    (2+4j)
d                         float      7.2
f                         int        -3
s                         str        hellowworld
s1                        str        hellow
s2                        str        world
startingTimeOfTheCourse   float      2.0
sumOfaAndb                int        8
v                         float      265.30199999999996
x                         float      5.7

Bool

a = True
b = True
c = False

%whos

Variable                  Type     Data/Info
--------------------------------------------
a                         bool     True
b                         bool     True
c                         bool     False
d                         float    7.2
f                         int      -3
s                         str      hellowworld
s1                        str      hellow
s2                        str      world
startingTimeOfTheCourse   float    2.0
sumOfaAndb                int      8
v                         float    265.30199999999996
x                         float    5.7

print(a and b)
print(a and c)
print(c and a)

True
False
False

d = a or c
print(d)

True

not(a)

False

not(b)

False

not(c)

True

t = not(d)

type(t)

bool

print(t)

False

not((a and b) or (c or d))

False

Comparisons

print(2<3)

True

c = 2<3
print(type(c))
print(c)

<class 'bool'>
True

d = 3==4

print(d)

False

3==3.0

True

x = 4
y = 9
z = 8.3
r = -3

(x<y) and (z<y) or (r==x)

True

(r==x) and (x<y) or (z>y)

False

(True or False) and False # and first and then or

False

print((not(2!=3)and Ture)or(False and True))

False

print(round(4.556))

5

print(round(4.345))

4

print(round(4.556389,3))

4.556

divmod(22,10)

(2, 2)

G = divmod(34,9)

type(G)

tuple

print(G)

(3, 7)

G[0]

3

G[1]

7

34//9

3

34%9 #remainder

7

isinstance(3,int) # is object a type of sth?

True

isinstance(3.4,(float,int))

True

isinstance(2+3j,(int,float,str,complex))

True

pow(2,4) # same with 2**4

16

2**4

16

pow(2,4,7) #2**4%7

2

x = input("enter a number :")

enter a number :56

type(x)

str

x = int(x) #change the type of x as integer

type(x)

int

print(x-34)

22

a = float(input("Enter a real number :"))

Enter a real number :12.5

type(a)

float

b = float(input("Enter a real number : "))

Enter a real number : abc

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[94], line 1
----> 1 b = float(input("Enter a real number : "))

ValueError: could not convert string to float: 'abc'

pow? #don't know how to use the function then add ?

  Cell In[98], line 1
    pow? #don't know how to use the function then add ?
       ^
SyntaxError: invalid syntax

help(input) #don't know how to use the function then use help()

Help on method raw_input in module ipykernel.kernelbase:

raw_input(prompt='') method of ipykernel.ipkernel.IPythonKernel instance
    Forward raw_input to frontends
    
    Raises
    ------
    StdinNotImplementedError if active frontend doesn't support stdin.

a = int(input())
b = int(input())
if a>b:
    print(a)
    print("I am still inside if condition")
print("I am outside the if condition") # diff block which not depend on if condition/ regardless of the result, should print this

10
45
I am outside the if condition

a = int(input())
b = int(input())
if a>b:
    print(a)
if b>a:
    print(b)

22
4
22

a = int(input())
b = int(input())
if a>b:
    print(a)
    print("if part")
else:
    print(b)
    print("else part")

10
10
10
else part

a = 10
b = 10
if a==b:
    print("Equal")
elif a>b:
    print("A")
else:
    print("B")
print("Not in if")

Equal
Not in if

a = int(input("Enter Marks :"))
if a >= 85:
    print("A Grade")
elif (a < 85) and (a >= 80): #write prenthesis makes it more readable
    print("A- Grade")
elif (a < 80) and (a >= 75):
    print("B Grade")
elif (a < 75) and (a >= 70):
    print("B- Grade")
else:
    print("Below Average")

Enter Marks :64
Below Average

#esle 안쓰고 else 사용하기
a = 3
if a>10:
    print(">10")
elif not(a>10):
    print("Else part")

Else part

a = int(input())
if a > 10:
    print(">10")
    print("Inside the top if")
    if a > 20:
        print(">20")
        print("Inside the nested if")
        if a>30:
            print(">30")
            print("inside the nested if of nested if")
        else:
            print("<=30")
            print("inside the else part of nested if of nested if")
    else:
        print("<=20")
        print("Inside the else part of nested if")
print("Outside all ifs")

25
>10
Inside the top if
>20
Inside the nested if
<=30
inside the else part of nested if of nested if
Outside all ifs

#single line comment
""" Multi line comment
User will enter a floating point number let say 238.915. 
Your task is to find out the integer portion before the point (in this case 238)
and then check if that integer portion is an even number or not
"""
x = float(input("Enter a real number :"))
y = round(x)
if x>0:
    if y>x:
        intPortion = y-1 #29.6
    else:
        intPortion = y
else:
    if y<x:
        intPortion = y+1
    else:
        intPortion = y

if intPortion%2 == 0:
    print("Evne")
else:
    print("Odd")

Enter a real number :-87.3
Odd

round(-9.3)

-9

round(-9.6)

-10

n = int(input())
i = 1
while (i < n):
    print(i**2)
    print("This is iteration number:", i)
    i += 1 #i = i+1
print("Loop done")

5
1
This is iteration number: 1
4
This is iteration number: 2
9
This is iteration number: 3
16
This is iteration number: 4
Loop done

n = 10
i = 1
while True:
    if i%9 == 0:
        print("Inside if")
        break
    else:
        print("Inside else")
        i = i+1
print("done")

Inside else
Inside else
Inside else
Inside else
Inside else
Inside else
Inside else
Inside else
Inside if
done

n = 10
i = 1
while True:
    if i%9 != 0:
        print("inside if")
        i +=1
        continue
    print("something")
    print("somethingelse")
    break
    
print("done")

inside if
inside if
inside if
inside if
inside if
inside if
inside if
inside if
something
somethingelse
done

L = []
for i in range(0,10,2): #0 start/10 end/ 2 step size
    print(i)
    L.append(i**2)
print(L)

0
2
4
6
8
[0, 4, 16, 36, 64]

S = {"apple", 4.9, "cherry"}
i = 1
for x in S: #as long as the x in S
    print(x)
    i += 1
    if i == 3:
        break
    else:
        pass
else:
    print("Loop terminates with success")
print("Out side the loop")

apple
4.9
Out side the loop

D = {"A":10, "B":-19, "C":"abc"}
for x in D:
    print(x, D[x])

A 10
B -19
C abc

""" Given a list of numbers i.e. [1,2,4,-5,7,9,3,2], make another list
that contains all the items in sorted order from min to max. i.e. your 
result will be another list like [-5,1,2,2,3,4,7,9]
"""
L = [1,2,4,-5,7,9,3,2]
for j in range(len(L)): #length of L
    m = L[j]
    idx = j
    c = j
    for i in range(j,len(L)):
        if L[i]<m:
            m = L[i]
            idx = c
        c += 1
    tmp = L[j]
    L[j] = m
    L[idx] = tmp
print(L)

[-5, 1, 2, 2, 3, 4, 7, 9]

#Refer stackoverflow answers
L = [1,2,4,-5,7,9,3,2]
m = L[0]
idx = 0
for i in L:
    if i<m:
        m = i
    idx += 1
print(idx, m)

8 -5

Functions

def printSuccess():
    print("I am done")
    print("send me another task")

printSuccess()

I am done
send me another task

3+8

11

printSuccess()

I am done
send me another task

def printSuccess2():
    """This funcion is doing nothing except printing a message.
    That message is "hellow"
    """ #this is doc string #use doc string whenever use functions
    print("hellow")

help(printSuccess2)

Help on function printSuccess2 in module __main__:

printSuccess2()
    This funcion is doing nothing except printing a message.
    That message is "hellow"

printSuccess2()

hellow

def printMessage(msg):
    """The function prints the message supplied by the user
    or prints that msg is not in the form of string"""

    if isinstance(msg,str):
        print(msg)
    else:
        print("Your input argument is not string")
        print("Here is the type of what you have supplied :", type(msg))

help(printMessage)

Help on function printMessage in module __main__:

printMessage(msg)
    The function prints the message supplied by the user
    or prints that msg is not in the form of string

printMessage??

printMessage("This is the message")

This is the message

printMessage(23)

Your input argument is not string
Here is the type of what you have supplied : <class 'int'>

y = "hellow there"
printMessage(y)

hellow there

#multiple arguments
def mypow(a,b):
    """this function compute power just like builtin pow function"""
    c = a**b
    print(c)

mypow?

mypow(3,4)

81

def checkArgs(a,b,c):
    if isinstance(a,(int,float)) and isinstance(b,(int,float)) and isinstance(c,(int,float)):
        print((a+b+c)**2)
    else:
        print("Error: the input arguments are not of the expected types")

checkArgs(3,4,5)

144

checkArgs(3,4,"g")

Error: the input arguments are not of the expected types

checkArgs(3,4)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[177], line 1
----> 1 checkArgs(3,4)

TypeError: checkArgs() missing 1 required positional argument: 'c'

checkArgs(2,3,4,5)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[178], line 1
----> 1 checkArgs(2,3,4,5)

TypeError: checkArgs() takes 3 positional arguments but 4 were given

#Order of input arguments
def f(a,b,c):
    print("A is :", a)
    print("B is :", b)
    print("C is :", c)

#f(2,3,"game")
f(3,"game",2)

A is : 3
B is : game
C is : 2

#f(a = 2, b = 3, c ="game")
f(c = "game", a = 2, b = 3)

A is : 2
B is : 3
C is : game

#Function(return statement)
def myadd(a,b):
    sumValue = a+b
    return sumValue

d = myadd(2,3)
print(d)

5

variableOutSideTheFunction = 3

def g():
    variableOutSideTheFunction = 5
    print(variableOutSideTheFunction) # inside the function

g()

5

print(variableOutSideTheFunction) # outside the function

3

def g():
    variableOutSideTheFunction = 5
    #print(variableOutSideTheFunction) # inside the function

print(g()) #if print isn't set inside the function, then none will be result when print(that func()).
print(type(g()))

None
<class 'NoneType'>

def h():
    print("A")
    a = 3
    b = 5
    c = a+b
    print("something")
    return  # Like this if I dont return anything, it means exit the function right away
    print("B")
    print("C")

print(h())

A
something
None

def h():
    print("A")
    a = 3
    b = 5
    c = a+b
    print("something")
    return c
    print("B")
    print("C")

print(type(h()))

A
something
<class 'int'>

def r():
    a = 5
    b = 7
    d = "something"
    return a,b,d #can return multiple values

x,y,z = r()
print(x,y,z)

5 7 something

def myAddUniversal(*args):
    s = 0
    for i in range(len(args)):
        s += args[i] #s = s+args[i]
    return s

print(myAddUniversal(2,4,5))

11

def printAllVariableNamesAndValues(**args):# ** you'll receive key value pair list
    for x in args:
        print("Variable Name is :", x,"and Value is :", args[x])

printAllVariableNamesAndValues(a = 3, b = "B", c = "CCC", y = 6.7)

Variable Name is : a and Value is : 3
Variable Name is : b and Value is : B
Variable Name is : c and Value is : CCC
Variable Name is : y and Value is : 6.7

#default value
def gg(s=4):
    print(s) #once press shift+enter, s is assigned as 4

gg()

4

gg(56)

56

L = [1,2,3]
L2 = L
L2[0] = -9
print(L)

[-9, 2, 3]

def ff(L =[1,2]):
    for i in L:
        print(i)

L2 = [12,3,4]
ff()

1
2

ff(L2)

12
3
4

ff() #already default value is assigned as L = [1,2]

1
2

#Modules

import sys
sys.path.append('/Users/soyeonpark/ABC')

#import all functions
import my_universal_functions as myfs

#import one function
#from my_universal_functions import addAllNumerics

myfs.addAllNumerics??

c = myfs.addAllNumerics(2,3,4,6)

print(c)

15

myfs.myName

'Python Cours'

""" Given a list of numbers i.e. [1,2,4,-5,7,9,3,2], make another list
that contains all the items in sorted order from min to max. i.e. your 
result will be another list like [-5,1,2,2,3,4,7,9]
"""

' Given a list of numbers i.e. [1,2,4,-5,7,9,3,2], make another list\nthat contains all the items in sorted order from min to max. i.e. your \nresult will be another list like [-5,1,2,2,3,4,7,9]\n'

def findMin(L, startIndx):
    m = L[startIndx]
    idx = startIndx
    for i in range(startIndx,len(L)):
        x = L[i]
        if x<m:
            m = x
            idx = i
        else:
            pass
        i += 1
    return m, idx

a,b = findMin([2,3,4,0,9])

print(a,b)

0 3

def swapValues(L,idx1,idx2):
    tmp = L[idx1]
    L[idx1] = L[idx2]
    L[idx2] = tmp
    return L

L = [2,3,6,7]
L2 = swapValues(L,1,3)
print(L2)

[2, 7, 6, 3]

from my_universal_functions import checkIfNotNumeric
def sortList(L):
    if not(checkIfNotNumeric2(L)):
        print("Error: List does not contain numeric values")
        return
    else:
        c = 0
        for x in L:
            m, idx = findMin(L, c)
            L = swapValues(L,c,idx)
            c += 1
    return L

L2 = sortList([2,1,5,3,-8,17])
print(L2)

[-8, 1, 2, 3, 5, 17]

checkIfNotNumeric??

checkIfNotNumeric2([2,1,5,3,-8,17])

True

def checkIfNotNumeric2(L):
    for x in L:
        if not(isinstance(x, (int, float))):
            return False
    return True

#String
s = "Python is a good language"
t = 'Its good for data science'

type(s)

str

print(s)

Python is a good language

print("hellow", 12, "hellow2", 'who are you', 5.9)

hellow 12 hellow2 who are you 5.9

v = s + " " + t #str+str+str
print(v)

Python is a good language Its good for data science

price = 12
s = "The price of this book"
v = s + ' is: '+ str(price) #str+str+int X --> str(int) 
print(v)
print(s,"is:", price) #print() automatically add space between arguments/ in thic case, don't need to chage the type

The price of this book is: 12
The price of this book is: 12

#String(Multi line String)
a = """this is line 1
this is line 2
this is last line and this line is 3"""
print(a)

this is line 1
this is line 2
this is last line and this line is 3

print(""" The following options are available: 
            -a      :does nothing
            -b      :also does nothing
""")

 The following options are available: 
            -a      :does nothing
            -b      :also does nothing

s = "How are you and who are you"
print(s[5])

r

print(type(s[5]))

<class 'str'>

s[3:8]

' are '

s[-1] #negative index is starting from right / -1 is the last letter

'u'

s[-12:-3]

' who are '

s[1] = "e"  # it's not possible. it's inmutable(unchanable) once the string is declared

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[294], line 1
----> 1 s[1] = "e"

TypeError: 'str' object does not support item assignment

s[0:12:2] # it skips with the step size 2 till 12(excluded) s[start:end:step]

'Hwaeyu'

s

'How are you and who are you'

s[:12]

'How are you '

s[3:]

' are you and who are you'

s[1:12]

'ow are you '

s[::-1] # reverse the way

'uoy era ohw dna uoy era woH'

print(len(a))

66

print(len(a[3:8]))

5

a = "    abc def     hgq   asdgeg" 
b = a.strip() #remove space in the beginning and end
print(b)

abc def     hgq   asdgeg

a = "ABC deFg ;; sadfa QF"
b = a.lower() #change all the characters into lower case
print(b)

abc defg ;; sadfa qf

c = a.upper() # change to upper case
print(c)

ABC DEFG ;; SADFA QF

d = a.replace(";","*") #replace the first one to the second one
print(d)

ABC deFg ** sadfa QF

d = a.replace(";","**&&^^%%")
print(d)

ABC deFg **&&^^%%**&&^^%% sadfa QF

d = a.replace(";;","two semi colons")
print(d)

ABC deFg two semi colons sadfa QF

a = "abc;def;hgydfa;yy23" # want to separate them
L = a.split(";") #split elements by the standard " "
print(L) #abc is one element

['abc', 'def', 'hgydfa', 'yy23']

L[1]

'def'

#. and tap button --> appear all the functions that can be used
print(a.capitalize())

Abc;def;hgydfa;yy23

"abdAfadfGGQ".capitalize()

'Abdafadfggq'

help(a.count)

Help on built-in function count:

count(...) method of builtins.str instance
    S.count(sub[, start[, end]]) -> int
    
    Return the number of non-overlapping occurrences of substring sub in
    string S[start:end].  Optional arguments start and end are
    interpreted as in slice notation.

"abc" in "asdfsafsjflskfjabclskjf"

True

"abc" == "abc" #can use == at string

True

"abc" < "def" #what does it mean? --> following alphabet order in Python

True

"$%" < "*&"

True

"acd" not in "ackljlkfj"

True

print("we are learning "string" here")

  Cell In[323], line 1
    print("we are learning "string" here")
          ^
SyntaxError: invalid syntax. Perhaps you forgot a comma?

print("we are learning \"string\"here") #use backslash

we are learning "string"here

print('we are learning "string" here') # or use single quot 

we are learning "string" here

print("we are \n now on another line") # for another line, use \n

we are 
 now on another line

print("we are \t now on another line")

we are 	 now on another line

print("c:\name\drive") # here \n acts like for another line

c:
ame\drive

print(r"c:\name\drive") # use r ahead to accept it as a raw string

c:\name\drive

Data Structures

#List
L = [1,3,4.9,"name",3]
#Tuple
T = (1,3,4.9,"name",3)
#Set
S = {1,3,4.9,"name",3}
#Dictionary
D = {23:"twothree", 'B':43, 'C':'CCD'}

print("The type of L is ", type(L))
print("The type of T is ", type(T))
print("The type of S is ", type(S))
print("The type of D is ", type(D))

The type of L is  <class 'list'>
The type of T is  <class 'tuple'>
The type of S is  <class 'set'>
The type of D is  <class 'dict'>

print(L[1])
print(T[1])
print(3 in S)
print(D[23])

3
3
True
twothree

print(D['B'])

43

S # no duplicate

{1, 3, 4.9, 'name'}

L

[1, 3, 4.9, 'name', 3]

L[1:3]

[3, 4.9]

L[::-1]

[3, 'name', 4.9, 3, 1]

T[:3]

(1, 3, 4.9)

L = L + ["how", "are", 6, "you"] #add element

L

[1, 3, 4.9, 'name', 3, 'how', 'are', 6, 'you']

L.append(6.8) #add element

L

[1, 3, 4.9, 'name', 3, 'how', 'are', 6, 'you', 6.8]

T2 = ('a', 'b', 45)
T3 = T + T2  #combine tuples

T3

(1, 3, 4.9, 'name', 3, 'a', 'b', 45)

S

{1, 3, 4.9, 'name'}

S.add(56) #add element

S

{1, 3, 4.9, 56, 'name'}

S.update({23,"game",1}) #add multiple elements

S

{1, 23, 3, 4.9, 56, 'game', 'name'}

D

{23: 'twothree', 'B': 43, 'C': 'CCD'}

D['newKey'] = "newValue"

D

{23: 'twothree', 'B': 43, 'C': 'CCD', 'newKey': 'newValue'}

D2 = {"y":"YY", "z":10}

D3 = D + D2 #not possible to combine Dictionaries /But can update like D.update(D2)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[370], line 1
----> 1 D3 = D + D2 #not possible to combine Dictionaries
      3 D.update(D2)

TypeError: unsupported operand type(s) for +: 'dict' and 'dict'

L

[1, 3, 4.9, 'name', 3, 'how', 'are', 6, 'you', 6.8]

del L[3] #delete element

L

[1, 3, 4.9, 3, 'how', 'are', 6, 'you', 6.8]

S

{1, 23, 3, 4.9, 56, 'game', 'name'}

S.remove('game')

S

{1, 23, 3, 4.9, 56, 'name'}

D

{23: 'twothree', 'B': 43, 'C': 'CCD', 'newKey': 'newValue'}

del D['C']

D

{23: 'twothree', 'B': 43, 'newKey': 'newValue'}

#copy function
L

[1, 3, 4.9, 3, 'how', 'are', 6, 'you', 6.8]

L2 = L

L2 #is saved in the same memory. so if we change one element in one list then the other is also changed

[1, 3, 4.9, 3, 'how', 'are', 6, 'you', 6.8]

L2[2] = "four point nine"

L2

[1, 3, 'four point nine', 3, 'how', 'are', 6, 'you', 6.8]

L

[1, 3, 'four point nine', 3, 'how', 'are', 6, 'you', 6.8]

L2 = L.copy() # should use copy function to save different memory/ Same at Set, Dictionary (Tuple doesn't use it)

L2

[1, 3, 'four point nine', 3, 'how', 'are', 6, 'you', 6.8]

L

[1, 3, 'four point nine', 3, 'how', 'are', 6, 'you', 6.8]

L2[1] = 'one'

L2

[1, 'one', 'four point nine', 3, 'how', 'are', 6, 'you', 6.8]

L

[1, 3, 'four point nine', 3, 'how', 'are', 6, 'you', 6.8]

L3 = L[1:5] #L3 is completely in new memory

L3

[3, 'four point nine', 3, 'how']

L3[0] = "three"

L

[1, 3, 'four point nine', 3, 'how', 'are', 6, 'you', 6.8]

help(L.append)

Help on built-in function append:

append(object, /) method of builtins.list instance
    Append object to the end of the list.

L.clear?

L.pop?

L.reverse()

L

[6.8, 'you', 6, 'are', 'how', 3, 'four point nine', 3, 1]

L[::-1]

[1, 3, 'four point nine', 3, 'how', 'are', 6, 'you', 6.8]

D.items?

L

[6.8, 'you', 6, 'are', 'how', 3, 'four point nine', 3, 1]

T

(1, 3, 4.9, 'name', 3)

S

{1, 23, 3, 4.9, 56, 'name'}

D

{23: 'twothree', 'B': 43, 'newKey': 'newValue'}

D2 = {'A':L, 'B':T, 'C':S, 'D':D} # one data structure can have the other structure as element

D2['A'][3] #at the Value of A, want to know the third position

'are'

K = D2['D']

K

{23: 'twothree', 'B': 43, 'newKey': 'newValue'}

for x in K:
    print(x,K[x])

23 twothree
B 43
newKey newValue

L3 = [L,T,D,23,"game"] # it's also possible

type(L3[2])

dict

L3 = [x**2 for x in range(10)]

L3

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

S3 = {x**2 for x in range(2,20,3)}

S3

{4, 25, 64, 121, 196, 289}

"""Let say you are a teacher and you have different student 
records containing id fo a student and the marks list in each subject
where different students have taken different number of subjects .All
these records are in hard copy. You want to enter all the data in computer
and want to compute the average marks of each student and display"""

def getDataFromUser():
    D = {}
    while True:
        studentId = input("Enter student ID: ")
        marksList = input("Enter the marks by comma separated values: ")
        moreStudents = input('Enter "no" to quit insertion: ')
        if studentId in D:
            print(studentId, "is already inserted")
        else:
            D[studentId] = marksList.split(",")
        if moreStudents.lower() == "no":
            return D

studentData = getDataFromUser()

Enter student ID: 12
Enter the marks by comma separated values: 56,45,13
Enter "no" to quit insertion: asdf
Enter student ID: 45
Enter the marks by comma separated values: 44,55,66,77,4
Enter "no" to quit insertion: asdfa
Enter student ID: 12
Enter the marks by comma separated values: 45,45
Enter "no" to quit insertion: asdfg
12 is already inserted
Enter student ID: 23
Enter the marks by comma separated values: 45,45
Enter "no" to quit insertion: no

studentData

{'12': ['56', '45', '13'],
 '45': ['44', '55', '66', '77', '4'],
 '23': ['45', '45']}

def getAvgMarks(D):
    avgMarks = {}
    for x in D:
        L = D[x]
        s = 0
        for marks in L:
            s += int(marks)
        avgMarks[x] = s/len(L)
    return avgMarks

avgM = getAvgMarks(studentData)

avgM

{'12': 38.0, '45': 49.2, '23': 45.0}

for x in avgM:
    print("Student :", x, "got avg Marks as: ", avgM[x])

Student : 12 got avg Marks as:  38.0
Student : 45 got avg Marks as:  49.2
Student : 23 got avg Marks as:  45.0

Numpy

import numpy as np

a = np.array([1,2,3,5,7]) #can define data type as well / i = integer

b = np.array((2,3,5), dtype= 'f') # f = float

print(a)

[1 2 3 5 7]

type(a)

numpy.ndarray

print(b)

[2 3 5]

type(b)

numpy.ndarray

a.dtype #what's the data type of a?

dtype('int32')

b.dtype #what's the data type of b?

dtype('float32')

#Numpy(Dimension)
import numpy as np
a = np.array([[1,2,3],[4,5,6]]) #2 dimensional array

a.ndim

2

a[0,2]

3

B = np.array([[1,2,3],[2,4,5,9]])

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[452], line 1
----> 1 B = np.array([[1,2,3],[2,4,5,9]])

ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.

B.ndim

1

C = np.array([[[1,2,3],[4,5,6],[0,0,-1]],[[-1,-2,-3],[-4,-5,-6],[0,0,1]]])

C.ndim

3

C.shape # each 2 dimensional array has 3 array which have 3 items

(2, 3, 3)

C[1,0,2]

-3

C.shape[2]

3

A = np.array([2]) # [] 

A.ndim

1

B = np.array(3) # no array due to doenst have []

B.ndim

0

C.size # total number of elements

18

C.nbytes #how many totals number of bytes

144

A = np.arange(100) #np.arange()

print(A)

[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
 96 97 98 99]

A = np.arange(20,100,3) #(start, last(excluded), size) like for i in range(20,100,3)
print(A)

[20 23 26 29 32 35 38 41 44 47 50 53 56 59 62 65 68 71 74 77 80 83 86 89
 92 95 98]

print(range(10)) #range() never create a list

range(0, 10)

print(list(range(10))) # if you want list then write list

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

A = np.random.permutation(np.arange(10)) #the arrage will be shown as randomly
print(A)

[7 1 3 6 2 9 0 4 8 5]

np.random.randint?

v = np.random.randint(20,30) #some random integer btw 20 and 30

type(v)

int

print(v)

21

print(v)

21

A = np.random.rand(1000) # random number btw 0 to 999

import matplotlib.pyplot as plt

plt.hist(A, bins=100)

(array([ 8., 11., 10., 12.,  8., 14., 11., 12.,  6., 10.,  4., 10.,  7.,
        14., 11.,  8.,  8.,  7.,  8., 16., 15.,  8.,  6.,  9., 14.,  9.,
        10., 16.,  7.,  9.,  8.,  7., 12., 11., 10., 14.,  7., 14., 11.,
         4., 11., 11.,  9., 15., 13.,  9., 10., 12., 12., 13., 10.,  7.,
        10.,  7.,  9.,  8., 10.,  8.,  5.,  9.,  6.,  6.,  8., 12.,  8.,
        17., 14.,  7.,  7., 10., 14.,  7.,  9., 12., 13., 14.,  5., 11.,
        10., 11., 14.,  9., 11., 16.,  8.,  7.,  8., 12., 18.,  7.,  4.,
         7., 13.,  9.,  8., 12., 11., 14., 11.,  6.]),
 array([1.81229342e-04, 1.01664234e-02, 2.01516174e-02, 3.01368115e-02,
        4.01220055e-02, 5.01071996e-02, 6.00923936e-02, 7.00775877e-02,
        8.00627817e-02, 9.00479758e-02, 1.00033170e-01, 1.10018364e-01,
        1.20003558e-01, 1.29988752e-01, 1.39973946e-01, 1.49959140e-01,
        1.59944334e-01, 1.69929528e-01, 1.79914722e-01, 1.89899916e-01,
        1.99885110e-01, 2.09870304e-01, 2.19855498e-01, 2.29840692e-01,
        2.39825886e-01, 2.49811081e-01, 2.59796275e-01, 2.69781469e-01,
        2.79766663e-01, 2.89751857e-01, 2.99737051e-01, 3.09722245e-01,
        3.19707439e-01, 3.29692633e-01, 3.39677827e-01, 3.49663021e-01,
        3.59648215e-01, 3.69633409e-01, 3.79618603e-01, 3.89603797e-01,
        3.99588991e-01, 4.09574185e-01, 4.19559379e-01, 4.29544573e-01,
        4.39529767e-01, 4.49514961e-01, 4.59500155e-01, 4.69485350e-01,
        4.79470544e-01, 4.89455738e-01, 4.99440932e-01, 5.09426126e-01,
        5.19411320e-01, 5.29396514e-01, 5.39381708e-01, 5.49366902e-01,
        5.59352096e-01, 5.69337290e-01, 5.79322484e-01, 5.89307678e-01,
        5.99292872e-01, 6.09278066e-01, 6.19263260e-01, 6.29248454e-01,
        6.39233648e-01, 6.49218842e-01, 6.59204036e-01, 6.69189230e-01,
        6.79174425e-01, 6.89159619e-01, 6.99144813e-01, 7.09130007e-01,
        7.19115201e-01, 7.29100395e-01, 7.39085589e-01, 7.49070783e-01,
        7.59055977e-01, 7.69041171e-01, 7.79026365e-01, 7.89011559e-01,
        7.98996753e-01, 8.08981947e-01, 8.18967141e-01, 8.28952335e-01,
        8.38937529e-01, 8.48922723e-01, 8.58907917e-01, 8.68893111e-01,
        8.78878305e-01, 8.88863499e-01, 8.98848694e-01, 9.08833888e-01,
        9.18819082e-01, 9.28804276e-01, 9.38789470e-01, 9.48774664e-01,
        9.58759858e-01, 9.68745052e-01, 9.78730246e-01, 9.88715440e-01,
        9.98700634e-01]),
 <BarContainer object of 100 artists>)

B = np.random.randn(10000)
plt.hist(B, bins=200)

(array([  1.,   0.,   1.,   0.,   2.,   2.,   1.,   1.,   0.,   2.,   2.,
          0.,   0.,   5.,   4.,   1.,   5.,   2.,   5.,   3.,   4.,   7.,
          7.,   5.,   7.,   2.,  12.,   8.,   6.,  11.,  10.,  10.,  13.,
          9.,  11.,  23.,  19.,  24.,  17.,  18.,  30.,  37.,  29.,  31.,
         25.,  38.,  29.,  37.,  39.,  30.,  45.,  49.,  61.,  49.,  58.,
         54.,  65.,  44.,  78.,  64.,  68.,  82.,  73.,  85.,  82.,  84.,
         98.,  86., 104.,  88., 105., 103., 112., 102., 107., 129., 141.,
        148., 130., 130., 119., 134., 134., 132., 157., 146., 150., 144.,
        148., 151., 151., 170., 164., 145., 124., 148., 161., 146., 129.,
        133., 155., 151., 134., 134., 138., 124., 138., 119., 112., 119.,
        112., 116., 117., 109., 103., 112., 119.,  96.,  98.,  83.,  76.,
         87.,  71.,  79.,  67.,  64.,  58.,  62.,  52.,  70.,  66.,  51.,
         53.,  53.,  43.,  33.,  48.,  39.,  38.,  33.,  27.,  33.,  32.,
         22.,  21.,  22.,  13.,  13.,  21.,  15.,  12.,  16.,  17.,  10.,
         15.,   6.,   7.,   5.,   4.,  11.,   4.,   2.,   6.,   7.,   6.,
          3.,   0.,   3.,   2.,   1.,   3.,   4.,   0.,   2.,   1.,   1.,
          1.,   0.,   0.,   1.,   1.,   0.,   1.,   1.,   0.,   0.,   0.,
          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,
          0.,   1.]),
 array([-3.46107023, -3.42369044, -3.38631066, -3.34893087, -3.31155109,
        -3.2741713 , -3.23679152, -3.19941173, -3.16203195, -3.12465216,
        -3.08727238, -3.0498926 , -3.01251281, -2.97513303, -2.93775324,
        -2.90037346, -2.86299367, -2.82561389, -2.7882341 , -2.75085432,
        -2.71347454, -2.67609475, -2.63871497, -2.60133518, -2.5639554 ,
        -2.52657561, -2.48919583, -2.45181604, -2.41443626, -2.37705647,
        -2.33967669, -2.30229691, -2.26491712, -2.22753734, -2.19015755,
        -2.15277777, -2.11539798, -2.0780182 , -2.04063841, -2.00325863,
        -1.96587884, -1.92849906, -1.89111928, -1.85373949, -1.81635971,
        -1.77897992, -1.74160014, -1.70422035, -1.66684057, -1.62946078,
        -1.592081  , -1.55470122, -1.51732143, -1.47994165, -1.44256186,
        -1.40518208, -1.36780229, -1.33042251, -1.29304272, -1.25566294,
        -1.21828315, -1.18090337, -1.14352359, -1.1061438 , -1.06876402,
        -1.03138423, -0.99400445, -0.95662466, -0.91924488, -0.88186509,
        -0.84448531, -0.80710552, -0.76972574, -0.73234596, -0.69496617,
        -0.65758639, -0.6202066 , -0.58282682, -0.54544703, -0.50806725,
        -0.47068746, -0.43330768, -0.3959279 , -0.35854811, -0.32116833,
        -0.28378854, -0.24640876, -0.20902897, -0.17164919, -0.1342694 ,
        -0.09688962, -0.05950983, -0.02213005,  0.01524973,  0.05262952,
         0.0900093 ,  0.12738909,  0.16476887,  0.20214866,  0.23952844,
         0.27690823,  0.31428801,  0.35166779,  0.38904758,  0.42642736,
         0.46380715,  0.50118693,  0.53856672,  0.5759465 ,  0.61332629,
         0.65070607,  0.68808586,  0.72546564,  0.76284542,  0.80022521,
         0.83760499,  0.87498478,  0.91236456,  0.94974435,  0.98712413,
         1.02450392,  1.0618837 ,  1.09926349,  1.13664327,  1.17402305,
         1.21140284,  1.24878262,  1.28616241,  1.32354219,  1.36092198,
         1.39830176,  1.43568155,  1.47306133,  1.51044111,  1.5478209 ,
         1.58520068,  1.62258047,  1.65996025,  1.69734004,  1.73471982,
         1.77209961,  1.80947939,  1.84685918,  1.88423896,  1.92161874,
         1.95899853,  1.99637831,  2.0337581 ,  2.07113788,  2.10851767,
         2.14589745,  2.18327724,  2.22065702,  2.25803681,  2.29541659,
         2.33279637,  2.37017616,  2.40755594,  2.44493573,  2.48231551,
         2.5196953 ,  2.55707508,  2.59445487,  2.63183465,  2.66921443,
         2.70659422,  2.743974  ,  2.78135379,  2.81873357,  2.85611336,
         2.89349314,  2.93087293,  2.96825271,  3.0056325 ,  3.04301228,
         3.08039206,  3.11777185,  3.15515163,  3.19253142,  3.2299112 ,
         3.26729099,  3.30467077,  3.34205056,  3.37943034,  3.41681013,
         3.45418991,  3.49156969,  3.52894948,  3.56632926,  3.60370905,
         3.64108883,  3.67846862,  3.7158484 ,  3.75322819,  3.79060797,
         3.82798775,  3.86536754,  3.90274732,  3.94012711,  3.97750689,
         4.01488668]),
 <BarContainer object of 200 artists>)

C = np.random.rand(2,3) #creat random dimensional array

C

array([[0.28479749, 0.01496218, 0.21795265],
       [0.29856608, 0.95685224, 0.28837714]])

C.ndim

2

C = np.random.rand(2,3,4,2)

C.ndim

4

C

array([[[[0.19808352, 0.90750045],
         [0.13427161, 0.90885532],
         [0.72531304, 0.67907578],
         [0.82546805, 0.83697186]],

        [[0.58072095, 0.80740941],
         [0.71044031, 0.89191532],
         [0.83809592, 0.12233693],
         [0.61687587, 0.40968328]],

        [[0.81907427, 0.41232916],
         [0.16349493, 0.43935106],
         [0.12297962, 0.00511371],
         [0.30436043, 0.38564601]]],


       [[[0.83505318, 0.75894666],
         [0.79148087, 0.04404956],
         [0.4473328 , 0.674635  ],
         [0.22660527, 0.51864064]],

        [[0.16631482, 0.44773438],
         [0.76406986, 0.98746421],
         [0.17695519, 0.62339607],
         [0.78282051, 0.74379242]],

        [[0.39200303, 0.76973911],
         [0.89048465, 0.62529171],
         [0.83263492, 0.4479187 ],
         [0.38048678, 0.84980509]]]])

D = np.arange(100).reshape(4,25) #reshape arrange following (a,b)

D.shape

(4, 25)

D

array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,
        16, 17, 18, 19, 20, 21, 22, 23, 24],
       [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
        41, 42, 43, 44, 45, 46, 47, 48, 49],
       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,
        66, 67, 68, 69, 70, 71, 72, 73, 74],
       [75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
        91, 92, 93, 94, 95, 96, 97, 98, 99]])

D = np.arange(100).reshape(4,5,5)

D.shape

(4, 5, 5)

D

array([[[ 0,  1,  2,  3,  4],
        [ 5,  6,  7,  8,  9],
        [10, 11, 12, 13, 14],
        [15, 16, 17, 18, 19],
        [20, 21, 22, 23, 24]],

       [[25, 26, 27, 28, 29],
        [30, 31, 32, 33, 34],
        [35, 36, 37, 38, 39],
        [40, 41, 42, 43, 44],
        [45, 46, 47, 48, 49]],

       [[50, 51, 52, 53, 54],
        [55, 56, 57, 58, 59],
        [60, 61, 62, 63, 64],
        [65, 66, 67, 68, 69],
        [70, 71, 72, 73, 74]],

       [[75, 76, 77, 78, 79],
        [80, 81, 82, 83, 84],
        [85, 86, 87, 88, 89],
        [90, 91, 92, 93, 94],
        [95, 96, 97, 98, 99]]])

np.zeros?

np.ones?

#Numpy(Slicing)
A = np.arange(100)
print(A)

[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
 96 97 98 99]

b = A[3:10]
print(b)

[3 4 5 6 7 8 9]

b[0] = -1200

b

array([-1200,     4,     5,     6,     7,     8,     9])

A #it's also changed because it's in the same memory / That's the big difference btw ordinary list and slicing

array([    0,     1,     2, -1200,     4,     5,     6,     7,     8,
           9,    10,    11,    12,    13,    14,    15,    16,    17,
          18,    19,    20,    21,    22,    23,    24,    25,    26,
          27,    28,    29,    30,    31,    32,    33,    34,    35,
          36,    37,    38,    39,    40,    41,    42,    43,    44,
          45,    46,    47,    48,    49,    50,    51,    52,    53,
          54,    55,    56,    57,    58,    59,    60,    61,    62,
          63,    64,    65,    66,    67,    68,    69,    70,    71,
          72,    73,    74,    75,    76,    77,    78,    79,    80,
          81,    82,    83,    84,    85,    86,    87,    88,    89,
          90,    91,    92,    93,    94,    95,    96,    97,    98,
          99])

b = A[3:10].copy() # if want not to change / it's in the different memory

A[::5]

array([ 0,  5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80,
       85, 90, 95])

A[::-5]

array([99, 94, 89, 84, 79, 74, 69, 64, 59, 54, 49, 44, 39, 34, 29, 24, 19,
       14,  9,  4])

A[::-1]

array([   99,    98,    97,    96,    95,    94,    93,    92,    91,
          90,    89,    88,    87,    86,    85,    84,    83,    82,
          81,    80,    79,    78,    77,    76,    75,    74,    73,
          72,    71,    70,    69,    68,    67,    66,    65,    64,
          63,    62,    61,    60,    59,    58,    57,    56,    55,
          54,    53,    52,    51,    50,    49,    48,    47,    46,
          45,    44,    43,    42,    41,    40,    39,    38,    37,
          36,    35,    34,    33,    32,    31,    30,    29,    28,
          27,    26,    25,    24,    23,    22,    21,    20,    19,
          18,    17,    16,    15,    14,    13,    12,    11,    10,
           9,     8,     7,     6,     5,     4, -1200,     2,     1,
           0])

#I want to find where is the position of -1200
B = (A == -1200)*np.arange(A.size)
print(B)

[0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]

A.indices(-1200)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[574], line 1
----> 1 A.indices(-1200)

AttributeError: 'numpy.ndarray' object has no attribute 'indices'

idx = np.argwhere(A==-1200)[0][0]

idx

3

A[idx] = 3

A

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])

A = np.round(10*np.random.rand(5,4))

#A = np.random.rand(5,4)

#A

array([[0.4183266 , 0.01855416, 0.25106059, 0.86243359],
       [0.16981521, 0.31147646, 0.95850589, 0.55175295],
       [0.15552773, 0.10055072, 0.06009027, 0.82065224],
       [0.95136422, 0.74344842, 0.26952486, 0.02808109],
       [0.84601038, 0.01401601, 0.39694368, 0.2467477 ]])

A

array([[ 4.,  1.,  5.,  5.],
       [ 3.,  1.,  7.,  4.],
       [ 2.,  8.,  0.,  7.],
       [ 3.,  0., 10., 10.],
       [ 6.,  8.,  1.,  7.]])

A[1,2]

7.0

A[1,:] # for whole second row

array([3., 1., 7., 4.])

A[:,1] # for whole second column

array([1., 1., 8., 0., 8.])

Z = A[1:3,2:4] #row number 1 to row number 3 & column number 2 to column number 4

Z

array([[7., 4.],
       [0., 7.]])

Z.T # transpose 행과 열 바꾸기

array([[7., 0.],
       [4., 7.]])

import numpy.linalg as la #linear algebra library 역행렬구하기

la.inv(np.random.rand(3,3))

array([[-0.82473966,  4.33580269, -0.46464079],
       [ 1.50081031,  0.34083488, -0.36621829],
       [-0.8377294 , -1.67459439,  1.4754175 ]])

Z

array([[1., 5.],
       [5., 7.]])

Za = Z.sort(axis=0) #행 축으로 정렬 # 여기부터 다시 해보기 

Za

A.sort(axis=1) #열 축으로 정렬

A

array([[0., 0., 1., 2.],
       [1., 1., 3., 3.],
       [2., 4., 4., 4.],
       [5., 6., 6., 8.],
       [7., 8., 8., 9.]])

#Numpy(More Indexing)
A = np.arange(100)

B = A[[3,5,6]]

B

array([3, 5, 6])

B[0] = -4

B

array([-4,  5,  6])

A # A doesn't change becuase we use B = A[[3,5,6]]

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])

B = A[A<40] # access all elements less than 40

B

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
       34, 35, 36, 37, 38, 39])

B = A[(A<40) & (A>30)] # btw 30 and 40

B

array([31, 32, 33, 34, 35, 36, 37, 38, 39])

#& for list / and for single object
#/ for list / or for single object
#~ for list/ not for single object

#Numpy(Broadcasting)
A = np.round(10*np.random.rand(2,3))

A

array([[2., 8., 3.],
       [5., 4., 1.]])

A+3

array([[ 5., 11.,  6.],
       [ 8.,  7.,  4.]])

A+(np.arange(2).reshape(2,1)) 

array([[2., 8., 3.],
       [6., 5., 2.]])

print(np.arange(2))

[0 1]

#stacks
B= np.round(10*np.random.rand(2,2))

A

array([[2., 8., 3.],
       [5., 4., 1.]])

B

array([[5., 9.],
       [4., 4.]])

C = np.hstack((A,B)) #가로로

C

array([[2., 8., 3., 5., 9.],
       [5., 4., 1., 4., 4.]])

A = np.random.permutation(np.arange(10))

A

array([2, 3, 8, 6, 4, 5, 7, 1, 0, 9])

A.sort() #ascending 오름차순

A

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

np.sort(A)

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

A.sort()

A=A[::-1] #descending 내림차순

A

array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])

A = np.array(["abc",'howare you','u765','132r'])

A.sort() # possible in strings like alphabet order

A

array(['132r', 'abc', 'howare you', 'u765'], dtype='<U10')

#Numpy(Speed: ufuncs)  numpy function is faster for large number/elements
B= np.random.rand(1000000)
%timeit sum(B)
%timeit np.sum(B) #same with B.sum()

38.4 ms ± 144 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
184 µs ± 1.86 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

def mySum(G):
    s = 0
    for x in G:
        s+=x
    return s

%timeit mySum(B)

43.5 ms ± 506 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

Pandas

import pandas as pd

print(pd.__version__)

1.5.3

A = pd.Series([2,3,4,5], index = ['a','b','c','d']) # Series handle one dimensional array

A.values

array([2, 3, 4, 5])

type(A.values)

numpy.ndarray

type(A)

pandas.core.series.Series

A.index

Index(['a', 'b', 'c', 'd'], dtype='object')

A['a']

2

A['a':'c'] #using idex then the final idex is also included

a    2
b    3
c    4
dtype: int64

#Pandas(Series) using dictionary
grads_dict={'A':4, 'B':3.5, 'C':3, 'D':2.5}
grads = pd.Series(grads_dict)

grads.values

array([4. , 3.5, 3. , 2.5])

marks_dict = {'A':85, 'B':75, 'C':65, 'D':55}
marks = pd.Series(marks_dict)

marks

A    85
B    75
C    65
D    55
dtype: int64

marks['A']

85

marks[0:2]

A    85
B    75
dtype: int64

#Pandas(DataFrame)
D = pd.DataFrame({'Marks':marks, 'Grades':grads})

D

	Marks	Grades
A	85	4.0
B	75	3.5
C	65	3.0
D	55	2.5

D.T #can transpose as well

	A	B	C	D
Marks	85.0	75.0	65.0	55.0
Grades	4.0	3.5	3.0	2.5

D

	Marks	Grades
A	85	4.0
B	75	3.5
C	65	3.0
D	55	2.5

D.values

array([[85. ,  4. ],
       [75. ,  3.5],
       [65. ,  3. ],
       [55. ,  2.5]])

D.values[2,0] #row #3 column #1

65.0

D.columns

Index(['Marks', 'Grades'], dtype='object')

D

	Marks	Grades
A	85	4.0
B	75	3.5
C	65	3.0
D	55	2.5

D['ScaledMarks'] = 100*(D['Marks']/90) # want to add column

D

	Marks	Grades	ScaledMarks
A	85	4.0	94.444444
B	75	3.5	83.333333
C	65	3.0	72.222222
D	55	2.5	61.111111

del D['ScaledMarks'] #want to delete column

D

	Marks	Grades
A	85	4.0
B	75	3.5
C	65	3.0
D	55	2.5

G = D[D['Marks']>70] # want to pick some data

G

	Marks	Grades
A	85	4.0
B	75	3.5

#Pandas(NaN) -- deal with missing values (None)
A = pd.DataFrame([{'a':1, 'b':4}, {'b':-3, 'c':9}])

A

	a	b	c
0	1.0	4	NaN
1	NaN	-3	9.0

A.fillna(0) #fill all na value as 0

	a	b	c
0	1.0	4	0.0
1	0.0	-3	9.0

A.dropna #drop all the missing values

<bound method DataFrame.dropna of      a  b    c
0  1.0  4  NaN
1  NaN -3  9.0>

#Pandas(Indexing)
A = pd.Series(['a','b','c'], index = [1, 3, 5])

A[1]

'a'

A[1:3]

3    b
5    c
dtype: object

A.loc[1:3] #loc : use explicit indexes

1    a
3    b
dtype: object

A.iloc[1:3] #iloc: use implicit indexes 기본적으로 첫 번째 값 위치 0

3    b
5    c
dtype: object

D

	Marks	Grades
A	85	4.0
B	75	3.5
C	65	3.0
D	55	2.5

D.iloc[2,:]

Marks     65.0
Grades     3.0
Name: C, dtype: float64

D.iloc[::-1,:] # want to reverse all the values

	Marks	Grades
D	55	2.5
C	65	3.0
B	75	3.5
A	85	4.0

#Pandas(csv files)
import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer
df = pd.read_csv('/Users/soyeonpark/Downloads/covid_19_data.csv')

df.head(10) #first 10 records

	SNo	ObservationDate	Province/State	Country/Region	Last Update	Confirmed	Deaths	Recovered
0	1	01/22/2020	Anhui	Mainland China	1/22/2020 17:00	1.0	0.0	0.0
1	2	01/22/2020	Beijing	Mainland China	1/22/2020 17:00	14.0	0.0	0.0
2	3	01/22/2020	Chongqing	Mainland China	1/22/2020 17:00	6.0	0.0	0.0
3	4	01/22/2020	Fujian	Mainland China	1/22/2020 17:00	1.0	0.0	0.0
4	5	01/22/2020	Gansu	Mainland China	1/22/2020 17:00	0.0	0.0	0.0
5	6	01/22/2020	Guangdong	Mainland China	1/22/2020 17:00	26.0	0.0	0.0
6	7	01/22/2020	Guangxi	Mainland China	1/22/2020 17:00	2.0	0.0	0.0
7	8	01/22/2020	Guizhou	Mainland China	1/22/2020 17:00	1.0	0.0	0.0
8	9	01/22/2020	Hainan	Mainland China	1/22/2020 17:00	4.0	0.0	0.0
9	10	01/22/2020	Hebei	Mainland China	1/22/2020 17:00	1.0	0.0	0.0

df.drop(['SNo', 'Last Update'], axis = 1, inplace = True)
# want to delete column. 'axis=1' is do that on the column and 'implace=True' is apply this to df

df.head()

	ObservationDate	Province/State	Country/Region	Confirmed	Deaths	Recovered
0	01/22/2020	Anhui	Mainland China	1.0	0.0	0.0
1	01/22/2020	Beijing	Mainland China	14.0	0.0	0.0
2	01/22/2020	Chongqing	Mainland China	6.0	0.0	0.0
3	01/22/2020	Fujian	Mainland China	1.0	0.0	0.0
4	01/22/2020	Gansu	Mainland China	0.0	0.0	0.0

df.rename(columns = {'ObservationDate':'Date', 'Province/State':'Province', 'Country/Region':'Country'}, inplace = True)
#want to rename the column name

df.head()

	Date	Province	Country	Confirmed	Deaths	Recovered
0	01/22/2020	Anhui	Mainland China	1.0	0.0	0.0
1	01/22/2020	Beijing	Mainland China	14.0	0.0	0.0
2	01/22/2020	Chongqing	Mainland China	6.0	0.0	0.0
3	01/22/2020	Fujian	Mainland China	1.0	0.0	0.0
4	01/22/2020	Gansu	Mainland China	0.0	0.0	0.0

df['Date'] = pd.to_datetime(df['Date'])
#convert date format to use in Pandas

df.head()

	Date	Province	Country	Confirmed	Deaths	Recovered
0	2020-01-22	Anhui	Mainland China	1.0	0.0	0.0
1	2020-01-22	Beijing	Mainland China	14.0	0.0	0.0
2	2020-01-22	Chongqing	Mainland China	6.0	0.0	0.0
3	2020-01-22	Fujian	Mainland China	1.0	0.0	0.0
4	2020-01-22	Gansu	Mainland China	0.0	0.0	0.0

df.describe()

	Confirmed	Deaths	Recovered
count	3.064290e+05	306429.000000	3.064290e+05
mean	8.567091e+04	2036.403268	5.042029e+04
std	2.775516e+05	6410.938048	2.015124e+05
min	-3.028440e+05	-178.000000	-8.544050e+05
25%	1.042000e+03	13.000000	1.100000e+01
50%	1.037500e+04	192.000000	1.751000e+03
75%	5.075200e+04	1322.000000	2.027000e+04
max	5.863138e+06	112385.000000	6.399531e+06

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 306429 entries, 0 to 306428
Data columns (total 6 columns):
 #   Column     Non-Null Count   Dtype         
---  ------     --------------   -----         
 0   Date       306429 non-null  datetime64[ns]
 1   Province   228329 non-null  object        
 2   Country    306429 non-null  object        
 3   Confirmed  306429 non-null  float64       
 4   Deaths     306429 non-null  float64       
 5   Recovered  306429 non-null  float64       
dtypes: datetime64[ns](1), float64(3), object(2)
memory usage: 14.0+ MB

df = df.fillna('NA') # fill in the blank as NA

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 306429 entries, 0 to 306428
Data columns (total 6 columns):
 #   Column     Non-Null Count   Dtype         
---  ------     --------------   -----         
 0   Date       306429 non-null  datetime64[ns]
 1   Province   306429 non-null  object        
 2   Country    306429 non-null  object        
 3   Confirmed  306429 non-null  float64       
 4   Deaths     306429 non-null  float64       
 5   Recovered  306429 non-null  float64       
dtypes: datetime64[ns](1), float64(3), object(2)
memory usage: 14.0+ MB

df.head(10)

	Date	Province	Country	Confirmed	Deaths	Recovered
0	2020-01-22	Anhui	Mainland China	1.0	0.0	0.0
1	2020-01-22	Beijing	Mainland China	14.0	0.0	0.0
2	2020-01-22	Chongqing	Mainland China	6.0	0.0	0.0
3	2020-01-22	Fujian	Mainland China	1.0	0.0	0.0
4	2020-01-22	Gansu	Mainland China	0.0	0.0	0.0
5	2020-01-22	Guangdong	Mainland China	26.0	0.0	0.0
6	2020-01-22	Guangxi	Mainland China	2.0	0.0	0.0
7	2020-01-22	Guizhou	Mainland China	1.0	0.0	0.0
8	2020-01-22	Hainan	Mainland China	4.0	0.0	0.0
9	2020-01-22	Hebei	Mainland China	1.0	0.0	0.0

#EX:how many total confirmed case in each countries?
df2 = df.groupby('Country')[['Country','Confirmed','Deaths','Recovered']].sum(numeric_only=True).reset_index()
#Country 는 숫자 데이터가 아니라서 numeric_only=True 넣어줌

df2

	Country	Confirmed	Deaths	Recovered
0	Azerbaijan	1.0	0.0	0.0
1	('St. Martin',)	2.0	0.0	0.0
2	Afghanistan	17026442.0	669075.0	13464399.0
3	Albania	19768869.0	375955.0	13945256.0
4	Algeria	27684358.0	834464.0	18959299.0
...	...	...	...	...
224	West Bank and Gaza	41819444.0	440378.0	37003116.0
225	Yemen	962066.0	237613.0	506523.0
226	Zambia	13493953.0	205990.0	12625626.0
227	Zimbabwe	6484581.0	237234.0	5594887.0
228	occupied Palestinian territory	25.0	0.0	0.0

229 rows × 4 columns

#grupby 'Country' and 'Date'
df2 = df.groupby(['Country','Date'])[['Country','Date','Confirmed','Deaths','Recovered']].sum(numeric_only=True).reset_index()

df2

	Country	Date	Confirmed	Deaths	Recovered
0	Azerbaijan	2020-02-28	1.0	0.0	0.0
1	('St. Martin',)	2020-03-10	2.0	0.0	0.0
2	Afghanistan	2020-02-24	1.0	0.0	0.0
3	Afghanistan	2020-02-25	1.0	0.0	0.0
4	Afghanistan	2020-02-26	1.0	0.0	0.0
...	...	...	...	...	...
87276	occupied Palestinian territory	2020-03-12	0.0	0.0	0.0
87277	occupied Palestinian territory	2020-03-14	0.0	0.0	0.0
87278	occupied Palestinian territory	2020-03-15	0.0	0.0	0.0
87279	occupied Palestinian territory	2020-03-16	0.0	0.0	0.0
87280	occupied Palestinian territory	2020-03-17	0.0	0.0	0.0

87281 rows × 5 columns

df3 = df2[df2['Confirmed']>100] #want case which have confirmed more than 100

df3

	Country	Date	Confirmed	Deaths	Recovered
35	Afghanistan	2020-03-28	107.0	4.0	2.0
36	Afghanistan	2020-03-29	118.0	4.0	2.0
37	Afghanistan	2020-03-30	146.0	4.0	2.0
38	Afghanistan	2020-03-31	175.0	4.0	5.0
39	Afghanistan	2020-04-01	197.0	4.0	5.0
...	...	...	...	...	...
87269	Zimbabwe	2021-05-25	38706.0	1587.0	36517.0
87270	Zimbabwe	2021-05-26	38819.0	1589.0	36531.0
87271	Zimbabwe	2021-05-27	38854.0	1592.0	36541.0
87272	Zimbabwe	2021-05-28	38918.0	1592.0	36563.0
87273	Zimbabwe	2021-05-29	38933.0	1594.0	36578.0

75055 rows × 5 columns

Matplotlib

import matplotlib.pyplot as plt

x = np.linspace(0,10,1000)
y = np.sin(x)
plt.plot(x,y)

[<matplotlib.lines.Line2D at 0x16a730dd0>]

plt.scatter(x[::10],y[::10], color = 'red')

<matplotlib.collections.PathCollection at 0x16a9261d0>

# can make two plot
plt.plot(x,y,color='b')
plt.plot(x,np.cos(x),color='g')

[<matplotlib.lines.Line2D at 0x16aacddd0>]

#Project Covid 19 using matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer
df = pd.read_csv('/Users/soyeonpark/Downloads/covid_19_data.csv')

df

	SNo	ObservationDate	Province/State	Country/Region	Last Update	Confirmed	Deaths	Recovered
0	1	01/22/2020	Anhui	Mainland China	1/22/2020 17:00	1.0	0.0	0.0
1	2	01/22/2020	Beijing	Mainland China	1/22/2020 17:00	14.0	0.0	0.0
2	3	01/22/2020	Chongqing	Mainland China	1/22/2020 17:00	6.0	0.0	0.0
3	4	01/22/2020	Fujian	Mainland China	1/22/2020 17:00	1.0	0.0	0.0
4	5	01/22/2020	Gansu	Mainland China	1/22/2020 17:00	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...
306424	306425	05/29/2021	Zaporizhia Oblast	Ukraine	2021-05-30 04:20:55	102641.0	2335.0	95289.0
306425	306426	05/29/2021	Zeeland	Netherlands	2021-05-30 04:20:55	29147.0	245.0	0.0
306426	306427	05/29/2021	Zhejiang	Mainland China	2021-05-30 04:20:55	1364.0	1.0	1324.0
306427	306428	05/29/2021	Zhytomyr Oblast	Ukraine	2021-05-30 04:20:55	87550.0	1738.0	83790.0
306428	306429	05/29/2021	Zuid-Holland	Netherlands	2021-05-30 04:20:55	391559.0	4252.0	0.0

306429 rows × 8 columns

df.drop(['SNo','Last Update'], axis=1, inplace=True)

df.rename(columns = {'ObservationDate':'Date', 'Province/State':'Province', 'Country/Region':'Country'}, inplace = True)

df.head()

	Date	Province	Country	Confirmed	Deaths	Recovered
0	01/22/2020	Anhui	Mainland China	1.0	0.0	0.0
1	01/22/2020	Beijing	Mainland China	14.0	0.0	0.0
2	01/22/2020	Chongqing	Mainland China	6.0	0.0	0.0
3	01/22/2020	Fujian	Mainland China	1.0	0.0	0.0
4	01/22/2020	Gansu	Mainland China	0.0	0.0	0.0

df['Date'] = pd.to_datetime(df['Date'])

df.head()

	Date	Province	Country	Confirmed	Deaths	Recovered
0	2020-01-22	Anhui	Mainland China	1.0	0.0	0.0
1	2020-01-22	Beijing	Mainland China	14.0	0.0	0.0
2	2020-01-22	Chongqing	Mainland China	6.0	0.0	0.0
3	2020-01-22	Fujian	Mainland China	1.0	0.0	0.0
4	2020-01-22	Gansu	Mainland China	0.0	0.0	0.0

imputer = SimpleImputer(strategy='constant') #missing values 결측치처리 좀 더 알아보기
df2 = pd.DataFrame(imputer.fit_transform(df),columns=df.columns)

df2

	Date	Province	Country	Confirmed	Deaths	Recovered
0	2020-01-22	Anhui	Mainland China	1.0	0.0	0.0
1	2020-01-22	Beijing	Mainland China	14.0	0.0	0.0
2	2020-01-22	Chongqing	Mainland China	6.0	0.0	0.0
3	2020-01-22	Fujian	Mainland China	1.0	0.0	0.0
4	2020-01-22	Gansu	Mainland China	0.0	0.0	0.0
...	...	...	...	...	...	...
306424	2021-05-29	Zaporizhia Oblast	Ukraine	102641.0	2335.0	95289.0
306425	2021-05-29	Zeeland	Netherlands	29147.0	245.0	0.0
306426	2021-05-29	Zhejiang	Mainland China	1364.0	1.0	1324.0
306427	2021-05-29	Zhytomyr Oblast	Ukraine	87550.0	1738.0	83790.0
306428	2021-05-29	Zuid-Holland	Netherlands	391559.0	4252.0	0.0

306429 rows × 6 columns

# 왜 confirmed, deaths, recovered 가 안 나오지???
df3 = df2.groupby(['Country','Date'])[['Confirmed','Deaths','Recovered']].sum().reset_index() # only nemeric columns

df3.head()

	Country	Date	Confirmed	Deaths	Recovered
0	Azerbaijan	2020-02-28	1.0	0.0	0.0
1	('St. Martin',)	2020-03-10	2.0	0.0	0.0
2	Afghanistan	2020-02-24	1.0	0.0	0.0
3	Afghanistan	2020-02-25	1.0	0.0	0.0
4	Afghanistan	2020-02-26	1.0	0.0	0.0

type(df2['Confirmed'])

pandas.core.series.Series

countries = df3['Country'].unique()
len(countries)

229

for idx in range(0,len(countries)):
    C = df3[df3['Country']==regions[idx]].reset_index()
    plt.scatter(np.arange(0,len(C)),C['Confirmed'],color ='b', label='Confirmed')
    plt.scatter(np.arange(0,len(C)),C['Recovered'],color ='g', label='Recovered')
    plt.scatter(np.arange(0,len(C)),C['Deaths'],color ='r', label='Deaths')
    plt.title(countries[idx])
    plt.xlabel('Days since the first suspect')
    plt.ylabel('Number of cases')
    plt.legend() #범례
    plt.show() #그래프 보여줘

df4 = df3.groupby(['Date'])[['Confirmed','Deaths','Recovered']].sum().reset_index()

df4.head

<bound method NDFrame.head of           Date    Confirmed     Deaths    Recovered
0   2020-01-22        557.0       17.0         30.0
1   2020-01-23       1097.0       34.0         60.0
2   2020-01-24        941.0       26.0         39.0
3   2020-01-25       1437.0       42.0         42.0
4   2020-01-26       2118.0       56.0         56.0
..         ...          ...        ...          ...
489 2021-05-25  167848207.0  3485788.0  104824935.0
490 2021-05-26  168416423.0  3498544.0  105380748.0
491 2021-05-27  168970791.0  3511297.0  106078106.0
492 2021-05-28  169470725.0  3523117.0  106633069.0
493 2021-05-29  169951560.0  3533619.0  107140669.0

[494 rows x 4 columns]>

C = df4
plt.scatter(np.arange(0,len(C)),C['Confirmed'],color='b',label='Confirmed')
plt.scatter(np.arange(0,len(C)),C['Recovered'],color='g',label='Recovered')
plt.scatter(np.arange(0,len(C)),C['Deaths'],color='r',label='Deaths')
plt.title('world')
plt.xlabel('Days since the first suspect')
plt.ylabel('Number of cases')
plt.legend()
plt.show()